Genetic algorithms are inspired by Darwin's theory about evolution. Solution to a problem solved by genetic algorithms is evolved.Algorithm is started with a set of solutions (represented by chromosomes) calledpopulation. Solutions from one population are taken and used to form a new population. This is motivated by a hope, that the new population will be better than the old one. Solutions which are selected to form new solutions (offspring) are selected according to their fitness - the more suitable they are the more chances they have to reproduce. This is repeated until some condition (for example number of populations or improvement of the best solution) is satisfied. (http://www.obitko.com/tutorials/genetic-algorithms/ga-basic-description.php)
Definition (Class Summary)
This is repeated until some condition (for example number of populations or improvement of the best solution) is satisfied. (http://www.obitko.com/tutorials/genetic-algorithms/ga-basic-description.php)
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Examples or Activities
Visual Demo of a G.A.
http://www.rennard.org/alife/english/gavgb.html#rTit01